TY - GEN
T1 - Expected Regret Minimization for Bayesian Optimization with Student's-t Processes
AU - Clare, Conor
AU - Hawe, Glenn
AU - McClean, Sally
PY - 2020/6/26
Y1 - 2020/6/26
N2 - Student's-t Processes were recently proposed as a probabilistic alternative to Gaussian Processes for Bayesian optimization. Student's-t Processes are a generalization of Gaussian Processes, using an extra parameter v, which addresses Gaussian Processes' weaknesses. Separately, recent work used prior knowledge of a black-box function's global optimum f*, to create a new acquisition function for Bayesian optimization called Expected Regret Minimization. Gaussian Processes were then combined with Expected Regret Minimization to outperform existing models for Bayesian optimization. No published work currently exists for Expected Regret Minimization with Student's-t Processes. This research compares Expected Regret Minimization for Bayesian optimization, using Student's-t Processes versus Gaussian Processes. Both models are applied to four problems popular in mathematical optimization. Our work enhances Bayesian optimization by showing superior training regret minimization for Expected Regret Minimization, using Student's-t Processes versus Gaussian Processes.
AB - Student's-t Processes were recently proposed as a probabilistic alternative to Gaussian Processes for Bayesian optimization. Student's-t Processes are a generalization of Gaussian Processes, using an extra parameter v, which addresses Gaussian Processes' weaknesses. Separately, recent work used prior knowledge of a black-box function's global optimum f*, to create a new acquisition function for Bayesian optimization called Expected Regret Minimization. Gaussian Processes were then combined with Expected Regret Minimization to outperform existing models for Bayesian optimization. No published work currently exists for Expected Regret Minimization with Student's-t Processes. This research compares Expected Regret Minimization for Bayesian optimization, using Student's-t Processes versus Gaussian Processes. Both models are applied to four problems popular in mathematical optimization. Our work enhances Bayesian optimization by showing superior training regret minimization for Expected Regret Minimization, using Student's-t Processes versus Gaussian Processes.
UR - https://dl.acm.org/doi/10.1145/3430199.3430218
U2 - 10.1145/3430199.3430218
DO - 10.1145/3430199.3430218
M3 - Conference contribution
SN - 9781450375511
VL - June 2020
T3 - Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition
SP - 8
EP - 12
BT - AIPR 2020: Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition
PB - Association for Computing Machinery
T2 - 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition
Y2 - 26 June 2020 through 28 June 2020
ER -